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Modeling nitrate at domestic and public-supply well depths in the Central Valley, California

Environmental Science and Technology

By:
, , , ,
DOI: 10.1021/es405452q

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Abstract

Aquifer vulnerability models were developed to map groundwater nitrate concentration at domestic and public-supply well depths in the Central Valley, California. We compared three modeling methods for ability to predict nitrate concentration >4 mg/L: logistic regression (LR), random forest classification (RFC), and random forest regression (RFR). All three models indicated processes of nitrogen fertilizer input at the land surface, transmission through coarse-textured, well-drained soils, and transport in the aquifer to the well screen. The total percent correct predictions were similar among the three models (69–82%), but RFR had greater sensitivity (84% for shallow wells and 51% for deep wells). The results suggest that RFR can better identify areas with high nitrate concentration but that LR and RFC may better describe bulk conditions in the aquifer. A unique aspect of the modeling approach was inclusion of outputs from previous, physically based hydrologic and textural models as predictor variables, which were important to the models. Vertical water fluxes in the aquifer and percent coarse material above the well screen were ranked moderately high-to-high in the RFR models, and the average vertical water flux during the irrigation season was highly significant (p < 0.0001) in logistic regression.

Geospatial Extents

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Modeling nitrate at domestic and public-supply well depths in the Central Valley, California
Series title:
Environmental Science and Technology
DOI:
10.1021/es405452q
Volume
48
Issue:
10
Year Published:
2014
Language:
English
Publisher:
American Chemical Society
Contributing office(s):
National Water Quality Assessment Program
Description:
9 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Environmental Science and Technology
First page:
5643
Last page:
5651
Number of Pages:
9
Country:
United States
State:
California
Other Geospatial:
Central Valley